KB Labs

It's not just about data. It's about connectingdata with people.

About

KB Labs seeks to find new ways to combine the library’s digital cultural heritage collections and research,
with the latest
methods within machine learning. The lab is an initiative taken by the IT department at the Royal Danish
Library.

Here you will find different applications made by the Royal Danish Library to visualize, engage or
showcase the
different materials or collections that we have available, to inspire and deepen the knowledge of what
collections
we actually have, and hopefully expand the use of these.

At the moment, applications and builds found here are considered experimental projects, and as
such, can change
or even be taken down without warning. This includes the data being presented by the applications.

Feel free to
contact us, if you have any questions or wish to know more about a given
project.

Friends and family

KUB Datalabs. The website of all the different data labs at
Copenhagen Univesity Library.

DIGHUMLAB. DIGHUMLAB is a community supporting digital research
activities within the humanities and the
social sciences in Denmark.

NetLab. NetLab is a Danish research infrastructure community for the
study of internet materials.

British Library Labs. British Library labs website, that supports and
inspires the public use of the British
Library’s digital collections and data in exciting and innovative ways.

Europeana Labs. They offers openly
licensed datasets, free APIs and funding to those who have the imagination, skills and desire to play
with digital cultural heritage collections and use them in their research, educational or creative
projects.

Projects

Image collections presented as collages, with seamless zooming from full collection to full-screen
single
images. Context-sensitive meta data with link-back to the originating sources makes it easy to
explore
large collections.

Adams Illustrated Panorama of History is a piece of educational art
that is large measured physically (70 x 673 cm), electronically (1
gigapixel) and by information density (thousands of information
fragments). Due to its size, viewing and talking about it is a
challenge. We aim to handle that by using techniques such as OpenSeadragon
for seamless overview-to-detail visualisation

Spatial bookmark support for referencing

Explorative meta-data for further reading (try hovering over the
globe in the lower left corner)

Display-mode with random or selected areas of interest walks

The last two parts are under development, but this is labs so we
publish early. Enjoy!

A serendipitous presentation of 1 million newspaper pages from Mediestream. For your convenience,
the 20
terapixels from the scanned papers are packed into a single image. Only thing needed is to zoom a
bit.

Word2Vec is a high-dimensional word embedding based on an unsupervised machine learning algorithm
using a
simple neural network. It maps each unique word in a large text corpus to a vector.
The vector representation of the words reflects interesting semantic properties of the words.
Words that
appear in the same context will be close in the vector-space (similar words). But distance between
words
can also be used to find analogies. The word2vec demo features several corpora and a very large one
based
on over 65.000 Gutenberg E-books.

MeLOAR is a dedicated front end for specific LOAR collections. This MeLOAR instance is for the LOAR collection "Beretningsarkiv for Arkæologiske Undersøgelser", which is an account archive for archaeological pdfs.

MeLOAR offers keyword search and location search, shows the search results with facets, maps and highlights, and shows the highlights inside the pdfs as well.